How low-cost AI universal approximators reshape market efficiency

TL;DR

This paper explores how affordable AI universal approximators influence market efficiency by enabling more sophisticated trading strategies, potentially reducing arbitrage opportunities and reshaping market dynamics.

Contribution

It introduces a generalized framework for market efficiency that incorporates AI-driven models and discusses the implications for out-of-equilibrium market behavior.

Findings

AI systems enhance trading strategy complexity

Increased model sophistication impacts market efficiency

Challenges in modeling adaptive multi-agent market dynamics

Abstract

The efficient market hypothesis (EMH) famously stated that prices fully reflect the information available to traders. This critically depends on the transfer of information into prices through trading strategies. Traders optimise their strategy with models of increasing complexity that identify the relationship between information and profitable trades more and more accurately. Under specific conditions, the increased availability of low-cost universal approximators, such as AI systems, should be naturally pushing towards more advanced trading strategies, potentially making it harder and harder for inefficient traders to profit. In this paper, we leverage on a generalised notion of market efficiency, based on the definition of an equilibrium price process, that allows us to distinguish different levels of model complexity through investors' beliefs, and trading strategies optimisation, and discuss the relationship between AI-powered trading and the time-evolution of market efficiency. Finally, we outline the need for and the challenge of describing out-of-equilibrium market dynamics in an adaptive multi-agent environment.